Noise-Perception Multi-Frame Collaborative Network for Enhanced Polyp Detection in Endoscopic Videos DOI Open Access
Haoran Li,

Guoyong Zhen,

Chengqun Chu

et al.

Electronics, Journal Year: 2024, Volume and Issue: 14(1), P. 62 - 62

Published: Dec. 27, 2024

The accurate detection and localization of polyps during endoscopic examinations are critical for early disease diagnosis cancer prevention. However, the presence artifacts noise, along with high similarity between surrounding tissues in color, shape, texture complicates polyp video frames. To tackle these challenges, we deployed multivariate regression analysis to refine model introduced a Noise-Suppressing Perception Network (NSPNet) designed enhanced performance. NSPNet leverages wavelet transform enhance model’s resistance noise while improving multi-frame collaborative strategy dynamic videos, efficiently utilizing temporal information strengthen features across Specifically, High-Low Frequency Feature Fusion (HFLF) framework, which allows capture high-frequency details more effectively. Additionally, an improved STFT-LSTM Polyp Detection (SLPD) module that utilizes from sequences feature fusion environments. Lastly, integrated Image Augmentation (IAPD) improve performance on unseen data through preprocessing enhancement strategies. Extensive experiments demonstrate outperforms nine SOTA methods four datasets key metrics, including F1Score recall.

Language: Английский

Dual-channel compression mapping network with fused attention mechanism for medical image segmentation DOI Creative Commons
Xiaokang Ding, Kai Qian, Qile Zhang

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 14, 2025

Language: Английский

Citations

1
Artificial intelligence based real time colorectal cancer screening study: Polyp segmentation and classification using multi-house database DOI
Jothiraj Selvaraj,

U. Snekhalatha,

Nanda Amarnath Rajesh

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 99, P. 106928 - 106928

Published: Sept. 25, 2024

Language: Английский

Citations

7
Noise-Perception Multi-Frame Collaborative Network for Enhanced Polyp Detection in Endoscopic Videos DOI Open Access
Haoran Li,

Guoyong Zhen,

Chengqun Chu

et al.

Electronics, Journal Year: 2024, Volume and Issue: 14(1), P. 62 - 62

Published: Dec. 27, 2024

The accurate detection and localization of polyps during endoscopic examinations are critical for early disease diagnosis cancer prevention. However, the presence artifacts noise, along with high similarity between surrounding tissues in color, shape, texture complicates polyp video frames. To tackle these challenges, we deployed multivariate regression analysis to refine model introduced a Noise-Suppressing Perception Network (NSPNet) designed enhanced performance. NSPNet leverages wavelet transform enhance model’s resistance noise while improving multi-frame collaborative strategy dynamic videos, efficiently utilizing temporal information strengthen features across Specifically, High-Low Frequency Feature Fusion (HFLF) framework, which allows capture high-frequency details more effectively. Additionally, an improved STFT-LSTM Polyp Detection (SLPD) module that utilizes from sequences feature fusion environments. Lastly, integrated Image Augmentation (IAPD) improve performance on unseen data through preprocessing enhancement strategies. Extensive experiments demonstrate outperforms nine SOTA methods four datasets key metrics, including F1Score recall.

Language: Английский

Citations

0